Although normal chemical or petro-chemical plant operations are controlled using Advanced Process Control (APC) and are optimized with Real-Time Optimization (RTO), a large number of undesirable plant events still occur in processes at chemical or petro-chemical plants, which cost the process industry billions of dollars per year. These undesirable plant events include unexpected unit operation breakdown or plant shutdown due to equipment problems, feed materials quality change, faulty sensors/actuators, and human operation errors. Because of the large number of undesirable plant events, the development of root-cause-analysis technology that leads to quick and efficient identification of the root causes of these events would be of extreme benefit to the process industry. However, chemical and petro-chemical plants measure a formidable number of process variables in relation to plant events. As such, performing root-cause-analysis on a particular plant event using the historian dataset for these measured process variables presents a challenge for process engineers and operators. Prior art systems lack tools for quickly and efficiently performing root-cause-analysis on such a formidable number of measured process variables.
Further, prior art systems lack effective online models, such as first principles models, to calculate event indicators for identifying particular process variables to use in root-cause-analysis of a plant event. First principles models have been widely used offline in petroleum, chemical, and process industries for process design, simulation, and optimization over the last 30 years because of their accuracy and transparency in fundamental physical and chemical principles. Commercial engineering software for offline applications using first principles models have advanced tremendously over the last 30 years, and during this time, efforts have been made to also use first principles models online for real-time applications, such as online process optimization and control. First principles models have many well-known advantages over black-box models that are typically used online. These advantages include being more rigorous and reliable for simulating and predicting process behavior, providing broader coverage of complex nonlinearities, and providing better extrapolations. Using first principles models online to calculate or predict key performance indicators (KPIs) has been a long time goal for many process engineers and operators. However, efforts to use a first principles model online for real-time event prediction, prevention and root-cause-analysis applications have been a challenging task, and for prior art systems there still exists a gap between theory and practice.